Evolutionary Optimization Algorithms

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A clear and lucid bottom-up approach to the basic principles of evolutionary algorithms

Evolutionary algorithms (EAs) are a type of artificial intelligence. EAs are motivated by optimization processes that we observe in nature, such as natural selection, species migration, bird swarms, human culture, and ant colonies.

Provides a straightforward, bottom-up approach that assists the reader in obtaining a clearbut theoretically rigorousunderstanding of evolutionary algorithms, with an emphasis on implementation

Gives a careful treatment of recently developed EAsincluding opposition-based learning, artificial fish swarms, bacterial foraging, and many others and discusses their similarities and differences from more well-established EAs

Includes chapter-end problems plus a solutions manual available online for instructors

Offers simple examples that provide the reader with an intuitive understanding of the theory

Features source code for the examples available on the author's website

DAN SIMON is a Professor at Cleveland State University in the Department of Electrical and Computer Engineering. His teaching and research interests include control theory, computer intelligence, embedded systems, technical writing, and related subjects. He is the author of the book Optimal State Estimation (Wiley).